Data security is a top-of-mind concern for many organizations. As data breaches occur on a daily basis and companies pay millions of dollars in remediation costs, anything that can be done to minimize the probability of a breach may make good business sense.
One of the biggest challenges in data security today is the rapid growth in size and complexity of the average enterprise network. Organizations’ network infrastructure is no longer limited to desktop computers and servers. Instead, they are deploying a wide variety of other platforms and using these to process and store sensitive data. However, with new platforms come new risks. Each new environment that an organization’s security team needs to learn to configure, monitor, and secure is one more opportunity for an oversight or mistake to result in a data breach.
As network complexity grows, organizations must work to minimize the potential exposure of the sensitive data in their care. This requires an understanding of key data security concepts, such as what is tokenization, and how to implement them in an organization’s network environment.
The Growing Complexity of Business Networks
In the past, business networks were primarily composed of desktop computers and servers that were directly connected to the corporate WAN. This made security relatively simple and straightforward since organizations could implement a perimeter-focused approach to security and deploy the same security solutions across the enterprise network.
This is no longer the case. Organizations are increasingly adopting digital transformation initiatives designed to make operations more efficient and to better serve the needs of their customers. With these digital transformations comes the deployment of new hardware and creation of new deployment environments.
As organizations adopt cloud computing, deploy Internet of Things (IoT) devices to monitor remote locations (or make coffee), and embrace business use of mobile devices, enterprise networks grow increasingly complex. Now, 84% of organizations are operating multiple clouds, and an estimated 5.8 billion IoT devices are deployed by enterprises for business purposes. With this additional complexity comes challenges for data security.
Data Security Challenges in Complex Networks
In many cases, deployment of new hardware or standing up deployments in the cloud makes good sense from a business perspective. Cloud computing, for example, enables an organization to access extremely flexible and scalable computing resources without incurring the costs associated with deploying and maintaining their network infrastructure and ensuring system availability through redundancy.
However, as organizations spread their network footprint to new devices and systems, they can lose visibility and control over the sensitive data that these systems are accessing and processing. When sensitive customer data may be collected by IoT devices, processed and stored using cloud infrastructure, and possibly sent to on-premises data centers for internal use, it can be difficult to ensure that this data is appropriately secured against unauthorized access and potential exposure.
The task of securing this data over multiple platforms is further complicated by the fact that security solutions traditionally used for on-premises systems may not be appropriate for other deployment environments. In cloud environments, an organization lacks control of their low-level infrastructure and must rely on configuration settings and tools provided by their cloud services provider (CSP) to secure their cloud-based resources. Similarly, desktop security tools may not be usable on resource-constrained IoT and mobile devices connected directly to the public Internet.
Simplifying Data Security Using Tokenization
As the number and types of endpoints that sensitive data is processed and stored on increases, the greater the complexity and difficulty of securing it. However, in many cases not all of these platforms require access to a certain piece of sensitive data. Instead, they just need to be able to uniquely identify a particular record.
For example, when filling an ecommerce order, a number of different parties are involved, including those responsible for processing the payments, putting together the order, and shipping the parcel. However, all of these parties do not require access to the complete customer record for the order. The payment processor is the only one that needs the payment card data, the person filling the order is the only one that requires access to the list of ordered items, and the shipper is the only one that requires address information. By limiting access to sensitive data based upon need to know and job roles, an organization can dramatically decrease its potential exposure in a data breach. However, each of these of these individuals does need to be able to uniquely identify a particular purchase or customer.
This is where tokenization comes in. By replacing sensitive data with a token, anyone who does not require access to that piece of data cannot access it. However, those with a valid business need to do so retain the ability to access the data required to perform their duties.
Securing Sensitive Data
As network complexity grows, organizations need to adopt new approaches to ensuring data security. Data that is spread across a variety of platforms and devices, each with their own unique security challenges, is much more likely to be compromised and exposed in a data breach.
Tokenization offers a solution to this problem by replacing sensitive data with a token wherever the real data is not needed. By minimizing the number of devices that have access to data in this way, an organization can decrease the probability of this data being exposed in an expensive and damaging data breach.